Artificial intelligence-reported chest X-ray findings of culture-confirmed pulmonary tuberculosis in people with and without diabetes

Coralie Geric, Arman Majidulla, Gamuchirai Tavaziva, Ahsana Nazish, Saima Saeed, Andrea Benedetti, Aamir J. Khan, Faiz Ahmad Khan

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Objectives: We applied computer-aided detection (CAD) software for chest X-ray (CXR) analysis to determine if diabetes affects the radiographic presentation of tuberculosis. Methods: From March 2017-July 2018, we consecutively enrolled adults being evaluated for pulmonary tuberculosis in Karachi, Pakistan. Participants had same-day CXR, two sputum mycobacterial cultures, and random blood glucose measurement. We identified diabetes through self-report or glucose >11.1mMol/L. We included participants with culture-confirmed tuberculosis for this analysis. We used linear regression to estimate associations between CAD-reported tuberculosis abnormality score (range 0.00 to 1.00) and diabetes, adjusting for age, body mass index, sputum smear-status, and prior tuberculosis. We also compared radiographic abnormalities between participants with and without diabetes. Results: 63/272 (23%) of included participants had diabetes. After adjustment, diabetes was associated with higher CAD tuberculosis abnormality scores (p < 0.001). Diabetes was not associated with frequency of CAD-reported radiographic abnormalities apart from cavitary disease; participants with diabetes were more likely to have cavitary disease (74.6% vs 61.2% p = 0.07), particularly non-upper zone cavitary disease (17% vs 7.8%, p = 0.09). Conclusions: CAD analysis of CXR suggests diabetes is associated with more extensive radiographic abnormalities and with greater likelihood of cavities outside upper lung zones.

Original languageEnglish
Article number100365
JournalJournal of Clinical Tuberculosis and Other Mycobacterial Diseases
Volume31
DOIs
Publication statusPublished - May 2023
Externally publishedYes

Keywords

  • Chest X-ray
  • Deep learning
  • Diabetes
  • Tuberculosis

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